[1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.05 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.2 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.4 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.6 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.8 [1]
“T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.95
[1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.05 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.2 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.4 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.6 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.8 [1]
“T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.95
[1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.05 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.2 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.4 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.6 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.8 [1]
“T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.95
[1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.05 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.2 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.4 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.6 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.8 [1]
“T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.95
[1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.05 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.2 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.4 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.6 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.8 [1]
“T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.95
[1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.05 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.2 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.4 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.6 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.8 [1]
“T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.95
[1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.05 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.2 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.4 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.6 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.8 [1]
“T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.95
[1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.05 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.2 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.4 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.6 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.8 [1]
“T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.95
[1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.05 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.2 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.4 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.6 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.8 [1]
“T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.95
[1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.05 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.2 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.4 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.6 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.8 [1]
“T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.95
[1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.05 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.2 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.4 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.6 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.8 [1]
“T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.95
[1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.05 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.2 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.4 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.6 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.8 [1]
“T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.95
[1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.05 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.2 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.4 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.6 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.8 [1]
“T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.95
[1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.05 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.2 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.4 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.6 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.8 [1]
“T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.95
[1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.05 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.2 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.4 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.6 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.8 [1]
“T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.95
[1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.05 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.2 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.4 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.6 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.8 [1]
“T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.95
[1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.05 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.2 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.4 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.6 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.8 [1]
“T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.95
[1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.05 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.2 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.4 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.6 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.8 [1]
“T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.95
[1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.05 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.2 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.4 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.6 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.8 [1]
“T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.95
[1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.05 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.2 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.4 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.6 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.8 [1]
“T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.95
[1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.05 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.2 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.4 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.6 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.8 [1]
“T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.95
[1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.05 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.2 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.4 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.6 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.8 [1]
“T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.95
[1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.05 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.2 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.4 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.6 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.8 [1]
“T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.95
[1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.05 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.2 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.4 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.6 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.8 [1]
“T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.95

Printing the
analysis outputs
par(op)
par(mfrow=c(1,2),cex=0.6)
rownames(totBaM) <- thenames
rownames(totDeM) <- thenames
rownames(toUnmatM) <- thenames
rownames(unalteredM) <- thenames
rownames(Decorrleated_FractionM) <- thenames
rownames(Base_FractionM) <- thenames
rownames(Unaltered_FractionM) <- thenames
rownames(sparcityM) <- thenames
rownames(Average_Latent_SizeM) <- thenames
rownames(SigDeM) <- thenames
rownames(La_SignificantM) <- thenames
rownames(pbKNNaucM) <- thenames
rownames(pbKNNaccM) <- thenames
colnames(totBaM) <- thr
colnames(totDeM) <- thr
colnames(toUnmatM) <- thr
colnames(unalteredM) <- thr
colnames(Decorrleated_FractionM) <- thr
colnames(Base_FractionM) <- thr
colnames(Unaltered_FractionM) <- thr
colnames(sparcityM) <- thr
colnames(Average_Latent_SizeM) <- thr
colnames(SigDeM) <- thr
colnames(La_SignificantM) <- thr
colnames(pbKNNaucM) <- thr
colnames(pbKNNaccM) <- thr
pander::pander(totFe)
450
pander::pander(totBaM)
| T_Blind_fast_LM_FALSE |
72 |
58 |
68 |
127 |
142 |
83 |
| T_Blind_fast_LM_TRUE |
48 |
20 |
27 |
96 |
127 |
83 |
| T_Blind_fast_RLM_FALSE |
72 |
58 |
68 |
127 |
142 |
83 |
| T_Blind_fast_RLM_TRUE |
48 |
20 |
27 |
96 |
127 |
83 |
| T_Blind_pearson_LM_FALSE |
55 |
35 |
71 |
128 |
142 |
83 |
| T_Blind_pearson_LM_TRUE |
37 |
23 |
23 |
98 |
127 |
83 |
| T_Blind_pearson_RLM_FALSE |
89 |
81 |
90 |
129 |
138 |
82 |
| T_Blind_pearson_RLM_TRUE |
75 |
72 |
65 |
88 |
124 |
82 |
| T_Blind_spearman_LM_FALSE |
68 |
69 |
60 |
121 |
137 |
52 |
| T_Blind_spearman_LM_TRUE |
54 |
55 |
19 |
94 |
119 |
52 |
| T_Blind_spearman_RLM_FALSE |
83 |
78 |
72 |
118 |
140 |
50 |
| T_Blind_spearman_RLM_TRUE |
73 |
75 |
43 |
94 |
122 |
50 |
| T_Driven_fast_LM_FALSE |
95 |
44 |
61 |
126 |
138 |
83 |
| T_Driven_fast_LM_TRUE |
66 |
60 |
38 |
100 |
127 |
83 |
| T_Driven_fast_RLM_FALSE |
95 |
44 |
61 |
126 |
138 |
83 |
| T_Driven_fast_RLM_TRUE |
66 |
60 |
38 |
100 |
127 |
83 |
| T_Driven_pearson_LM_FALSE |
52 |
61 |
61 |
125 |
138 |
83 |
| T_Driven_pearson_LM_TRUE |
53 |
48 |
40 |
99 |
127 |
83 |
| T_Driven_pearson_RLM_FALSE |
92 |
93 |
95 |
125 |
134 |
80 |
| T_Driven_pearson_RLM_TRUE |
83 |
73 |
73 |
103 |
123 |
80 |
| T_Driven_spearman_LM_FALSE |
67 |
60 |
67 |
119 |
134 |
51 |
| T_Driven_spearman_LM_TRUE |
66 |
54 |
30 |
96 |
123 |
51 |
| T_Driven_spearman_RLM_FALSE |
84 |
68 |
76 |
115 |
134 |
50 |
| T_Driven_spearman_RLM_TRUE |
76 |
76 |
65 |
98 |
125 |
50 |
pander::pander(totDeM)
| T_Blind_fast_LM_FALSE |
357 |
377 |
377 |
276 |
178 |
78 |
| T_Blind_fast_LM_TRUE |
380 |
422 |
421 |
306 |
194 |
79 |
| T_Blind_fast_RLM_FALSE |
357 |
377 |
377 |
276 |
178 |
78 |
| T_Blind_fast_RLM_TRUE |
380 |
422 |
421 |
306 |
194 |
79 |
| T_Blind_pearson_LM_FALSE |
381 |
407 |
367 |
276 |
178 |
78 |
| T_Blind_pearson_LM_TRUE |
390 |
414 |
424 |
305 |
194 |
79 |
| T_Blind_pearson_RLM_FALSE |
314 |
324 |
315 |
264 |
175 |
78 |
| T_Blind_pearson_RLM_TRUE |
334 |
335 |
339 |
290 |
189 |
79 |
| T_Blind_spearman_LM_FALSE |
362 |
361 |
383 |
280 |
175 |
52 |
| T_Blind_spearman_LM_TRUE |
370 |
368 |
428 |
316 |
192 |
52 |
| T_Blind_spearman_RLM_FALSE |
331 |
338 |
357 |
273 |
172 |
52 |
| T_Blind_spearman_RLM_TRUE |
342 |
341 |
394 |
315 |
190 |
52 |
| T_Driven_fast_LM_FALSE |
327 |
391 |
383 |
282 |
178 |
78 |
| T_Driven_fast_LM_TRUE |
357 |
363 |
410 |
306 |
190 |
79 |
| T_Driven_fast_RLM_FALSE |
327 |
391 |
383 |
282 |
178 |
78 |
| T_Driven_fast_RLM_TRUE |
357 |
363 |
410 |
306 |
190 |
79 |
| T_Driven_pearson_LM_FALSE |
375 |
370 |
384 |
282 |
178 |
78 |
| T_Driven_pearson_LM_TRUE |
371 |
379 |
408 |
306 |
190 |
79 |
| T_Driven_pearson_RLM_FALSE |
306 |
309 |
313 |
266 |
174 |
78 |
| T_Driven_pearson_RLM_TRUE |
323 |
329 |
327 |
284 |
186 |
79 |
| T_Driven_spearman_LM_FALSE |
357 |
365 |
373 |
288 |
174 |
52 |
| T_Driven_spearman_LM_TRUE |
357 |
369 |
412 |
314 |
184 |
52 |
| T_Driven_spearman_RLM_FALSE |
329 |
349 |
348 |
284 |
171 |
52 |
| T_Driven_spearman_RLM_TRUE |
342 |
338 |
369 |
308 |
182 |
52 |
pander::pander(toUnmatM)
| T_Blind_fast_LM_FALSE |
72 |
58 |
68 |
127 |
142 |
83 |
| T_Blind_fast_LM_TRUE |
48 |
20 |
27 |
96 |
127 |
83 |
| T_Blind_fast_RLM_FALSE |
72 |
58 |
68 |
127 |
142 |
83 |
| T_Blind_fast_RLM_TRUE |
48 |
20 |
27 |
96 |
127 |
83 |
| T_Blind_pearson_LM_FALSE |
55 |
35 |
71 |
128 |
142 |
83 |
| T_Blind_pearson_LM_TRUE |
37 |
23 |
23 |
98 |
127 |
83 |
| T_Blind_pearson_RLM_FALSE |
89 |
81 |
90 |
129 |
138 |
82 |
| T_Blind_pearson_RLM_TRUE |
75 |
72 |
65 |
88 |
124 |
82 |
| T_Blind_spearman_LM_FALSE |
68 |
69 |
60 |
121 |
137 |
52 |
| T_Blind_spearman_LM_TRUE |
54 |
55 |
19 |
94 |
119 |
52 |
| T_Blind_spearman_RLM_FALSE |
83 |
78 |
72 |
118 |
140 |
50 |
| T_Blind_spearman_RLM_TRUE |
73 |
75 |
43 |
94 |
122 |
50 |
| T_Driven_fast_LM_FALSE |
95 |
44 |
61 |
126 |
138 |
83 |
| T_Driven_fast_LM_TRUE |
66 |
60 |
38 |
100 |
127 |
83 |
| T_Driven_fast_RLM_FALSE |
95 |
44 |
61 |
126 |
138 |
83 |
| T_Driven_fast_RLM_TRUE |
66 |
60 |
38 |
100 |
127 |
83 |
| T_Driven_pearson_LM_FALSE |
52 |
61 |
61 |
125 |
138 |
83 |
| T_Driven_pearson_LM_TRUE |
53 |
48 |
40 |
99 |
127 |
83 |
| T_Driven_pearson_RLM_FALSE |
92 |
93 |
95 |
125 |
134 |
80 |
| T_Driven_pearson_RLM_TRUE |
83 |
73 |
73 |
103 |
123 |
80 |
| T_Driven_spearman_LM_FALSE |
67 |
60 |
67 |
119 |
134 |
51 |
| T_Driven_spearman_LM_TRUE |
66 |
54 |
30 |
96 |
123 |
51 |
| T_Driven_spearman_RLM_FALSE |
84 |
68 |
76 |
115 |
134 |
50 |
| T_Driven_spearman_RLM_TRUE |
76 |
76 |
65 |
98 |
125 |
50 |
pander::pander(unalteredM)
| T_Blind_fast_LM_FALSE |
93 |
73 |
73 |
174 |
272 |
372 |
| T_Blind_fast_LM_TRUE |
70 |
28 |
29 |
144 |
256 |
371 |
| T_Blind_fast_RLM_FALSE |
93 |
73 |
73 |
174 |
272 |
372 |
| T_Blind_fast_RLM_TRUE |
70 |
28 |
29 |
144 |
256 |
371 |
| T_Blind_pearson_LM_FALSE |
69 |
43 |
83 |
174 |
272 |
372 |
| T_Blind_pearson_LM_TRUE |
60 |
36 |
26 |
145 |
256 |
371 |
| T_Blind_pearson_RLM_FALSE |
136 |
126 |
135 |
186 |
275 |
372 |
| T_Blind_pearson_RLM_TRUE |
116 |
115 |
111 |
160 |
261 |
371 |
| T_Blind_spearman_LM_FALSE |
88 |
89 |
67 |
170 |
275 |
398 |
| T_Blind_spearman_LM_TRUE |
80 |
82 |
22 |
134 |
258 |
398 |
| T_Blind_spearman_RLM_FALSE |
119 |
112 |
93 |
177 |
278 |
398 |
| T_Blind_spearman_RLM_TRUE |
108 |
109 |
56 |
135 |
260 |
398 |
| T_Driven_fast_LM_FALSE |
123 |
59 |
67 |
168 |
272 |
372 |
| T_Driven_fast_LM_TRUE |
93 |
87 |
40 |
144 |
260 |
371 |
| T_Driven_fast_RLM_FALSE |
123 |
59 |
67 |
168 |
272 |
372 |
| T_Driven_fast_RLM_TRUE |
93 |
87 |
40 |
144 |
260 |
371 |
| T_Driven_pearson_LM_FALSE |
75 |
80 |
66 |
168 |
272 |
372 |
| T_Driven_pearson_LM_TRUE |
79 |
71 |
42 |
144 |
260 |
371 |
| T_Driven_pearson_RLM_FALSE |
144 |
141 |
137 |
184 |
276 |
372 |
| T_Driven_pearson_RLM_TRUE |
127 |
121 |
123 |
166 |
264 |
371 |
| T_Driven_spearman_LM_FALSE |
93 |
85 |
77 |
162 |
276 |
398 |
| T_Driven_spearman_LM_TRUE |
93 |
81 |
38 |
136 |
266 |
398 |
| T_Driven_spearman_RLM_FALSE |
121 |
101 |
102 |
166 |
279 |
398 |
| T_Driven_spearman_RLM_TRUE |
108 |
112 |
81 |
142 |
268 |
398 |
pander::pander(Decorrleated_FractionM)
| T_Blind_fast_LM_FALSE |
0.793 |
0.838 |
0.838 |
0.613 |
0.396 |
0.173 |
| T_Blind_fast_LM_TRUE |
0.844 |
0.938 |
0.936 |
0.680 |
0.431 |
0.176 |
| T_Blind_fast_RLM_FALSE |
0.793 |
0.838 |
0.838 |
0.613 |
0.396 |
0.173 |
| T_Blind_fast_RLM_TRUE |
0.844 |
0.938 |
0.936 |
0.680 |
0.431 |
0.176 |
| T_Blind_pearson_LM_FALSE |
0.847 |
0.904 |
0.816 |
0.613 |
0.396 |
0.173 |
| T_Blind_pearson_LM_TRUE |
0.867 |
0.920 |
0.942 |
0.678 |
0.431 |
0.176 |
| T_Blind_pearson_RLM_FALSE |
0.698 |
0.720 |
0.700 |
0.587 |
0.389 |
0.173 |
| T_Blind_pearson_RLM_TRUE |
0.742 |
0.744 |
0.753 |
0.644 |
0.420 |
0.176 |
| T_Blind_spearman_LM_FALSE |
0.804 |
0.802 |
0.851 |
0.622 |
0.389 |
0.116 |
| T_Blind_spearman_LM_TRUE |
0.822 |
0.818 |
0.951 |
0.702 |
0.427 |
0.116 |
| T_Blind_spearman_RLM_FALSE |
0.736 |
0.751 |
0.793 |
0.607 |
0.382 |
0.116 |
| T_Blind_spearman_RLM_TRUE |
0.760 |
0.758 |
0.876 |
0.700 |
0.422 |
0.116 |
| T_Driven_fast_LM_FALSE |
0.727 |
0.869 |
0.851 |
0.627 |
0.396 |
0.173 |
| T_Driven_fast_LM_TRUE |
0.793 |
0.807 |
0.911 |
0.680 |
0.422 |
0.176 |
| T_Driven_fast_RLM_FALSE |
0.727 |
0.869 |
0.851 |
0.627 |
0.396 |
0.173 |
| T_Driven_fast_RLM_TRUE |
0.793 |
0.807 |
0.911 |
0.680 |
0.422 |
0.176 |
| T_Driven_pearson_LM_FALSE |
0.833 |
0.822 |
0.853 |
0.627 |
0.396 |
0.173 |
| T_Driven_pearson_LM_TRUE |
0.824 |
0.842 |
0.907 |
0.680 |
0.422 |
0.176 |
| T_Driven_pearson_RLM_FALSE |
0.680 |
0.687 |
0.696 |
0.591 |
0.387 |
0.173 |
| T_Driven_pearson_RLM_TRUE |
0.718 |
0.731 |
0.727 |
0.631 |
0.413 |
0.176 |
| T_Driven_spearman_LM_FALSE |
0.793 |
0.811 |
0.829 |
0.640 |
0.387 |
0.116 |
| T_Driven_spearman_LM_TRUE |
0.793 |
0.820 |
0.916 |
0.698 |
0.409 |
0.116 |
| T_Driven_spearman_RLM_FALSE |
0.731 |
0.776 |
0.773 |
0.631 |
0.380 |
0.116 |
| T_Driven_spearman_RLM_TRUE |
0.760 |
0.751 |
0.820 |
0.684 |
0.404 |
0.116 |
pander::pander(Base_FractionM)
| T_Blind_fast_LM_FALSE |
0.1600 |
0.1289 |
0.1511 |
0.282 |
0.316 |
0.184 |
| T_Blind_fast_LM_TRUE |
0.1067 |
0.0444 |
0.0600 |
0.213 |
0.282 |
0.184 |
| T_Blind_fast_RLM_FALSE |
0.1600 |
0.1289 |
0.1511 |
0.282 |
0.316 |
0.184 |
| T_Blind_fast_RLM_TRUE |
0.1067 |
0.0444 |
0.0600 |
0.213 |
0.282 |
0.184 |
| T_Blind_pearson_LM_FALSE |
0.1222 |
0.0778 |
0.1578 |
0.284 |
0.316 |
0.184 |
| T_Blind_pearson_LM_TRUE |
0.0822 |
0.0511 |
0.0511 |
0.218 |
0.282 |
0.184 |
| T_Blind_pearson_RLM_FALSE |
0.1978 |
0.1800 |
0.2000 |
0.287 |
0.307 |
0.182 |
| T_Blind_pearson_RLM_TRUE |
0.1667 |
0.1600 |
0.1444 |
0.196 |
0.276 |
0.182 |
| T_Blind_spearman_LM_FALSE |
0.1511 |
0.1533 |
0.1333 |
0.269 |
0.304 |
0.116 |
| T_Blind_spearman_LM_TRUE |
0.1200 |
0.1222 |
0.0422 |
0.209 |
0.264 |
0.116 |
| T_Blind_spearman_RLM_FALSE |
0.1844 |
0.1733 |
0.1600 |
0.262 |
0.311 |
0.111 |
| T_Blind_spearman_RLM_TRUE |
0.1622 |
0.1667 |
0.0956 |
0.209 |
0.271 |
0.111 |
| T_Driven_fast_LM_FALSE |
0.2111 |
0.0978 |
0.1356 |
0.280 |
0.307 |
0.184 |
| T_Driven_fast_LM_TRUE |
0.1467 |
0.1333 |
0.0844 |
0.222 |
0.282 |
0.184 |
| T_Driven_fast_RLM_FALSE |
0.2111 |
0.0978 |
0.1356 |
0.280 |
0.307 |
0.184 |
| T_Driven_fast_RLM_TRUE |
0.1467 |
0.1333 |
0.0844 |
0.222 |
0.282 |
0.184 |
| T_Driven_pearson_LM_FALSE |
0.1156 |
0.1356 |
0.1356 |
0.278 |
0.307 |
0.184 |
| T_Driven_pearson_LM_TRUE |
0.1178 |
0.1067 |
0.0889 |
0.220 |
0.282 |
0.184 |
| T_Driven_pearson_RLM_FALSE |
0.2044 |
0.2067 |
0.2111 |
0.278 |
0.298 |
0.178 |
| T_Driven_pearson_RLM_TRUE |
0.1844 |
0.1622 |
0.1622 |
0.229 |
0.273 |
0.178 |
| T_Driven_spearman_LM_FALSE |
0.1489 |
0.1333 |
0.1489 |
0.264 |
0.298 |
0.113 |
| T_Driven_spearman_LM_TRUE |
0.1467 |
0.1200 |
0.0667 |
0.213 |
0.273 |
0.113 |
| T_Driven_spearman_RLM_FALSE |
0.1867 |
0.1511 |
0.1689 |
0.256 |
0.298 |
0.111 |
| T_Driven_spearman_RLM_TRUE |
0.1689 |
0.1689 |
0.1444 |
0.218 |
0.278 |
0.111 |
pander::pander(Unaltered_FractionM)
| T_Blind_fast_LM_FALSE |
0.207 |
0.1622 |
0.1622 |
0.387 |
0.604 |
0.827 |
| T_Blind_fast_LM_TRUE |
0.156 |
0.0622 |
0.0644 |
0.320 |
0.569 |
0.824 |
| T_Blind_fast_RLM_FALSE |
0.207 |
0.1622 |
0.1622 |
0.387 |
0.604 |
0.827 |
| T_Blind_fast_RLM_TRUE |
0.156 |
0.0622 |
0.0644 |
0.320 |
0.569 |
0.824 |
| T_Blind_pearson_LM_FALSE |
0.153 |
0.0956 |
0.1844 |
0.387 |
0.604 |
0.827 |
| T_Blind_pearson_LM_TRUE |
0.133 |
0.0800 |
0.0578 |
0.322 |
0.569 |
0.824 |
| T_Blind_pearson_RLM_FALSE |
0.302 |
0.2800 |
0.3000 |
0.413 |
0.611 |
0.827 |
| T_Blind_pearson_RLM_TRUE |
0.258 |
0.2556 |
0.2467 |
0.356 |
0.580 |
0.824 |
| T_Blind_spearman_LM_FALSE |
0.196 |
0.1978 |
0.1489 |
0.378 |
0.611 |
0.884 |
| T_Blind_spearman_LM_TRUE |
0.178 |
0.1822 |
0.0489 |
0.298 |
0.573 |
0.884 |
| T_Blind_spearman_RLM_FALSE |
0.264 |
0.2489 |
0.2067 |
0.393 |
0.618 |
0.884 |
| T_Blind_spearman_RLM_TRUE |
0.240 |
0.2422 |
0.1244 |
0.300 |
0.578 |
0.884 |
| T_Driven_fast_LM_FALSE |
0.273 |
0.1311 |
0.1489 |
0.373 |
0.604 |
0.827 |
| T_Driven_fast_LM_TRUE |
0.207 |
0.1933 |
0.0889 |
0.320 |
0.578 |
0.824 |
| T_Driven_fast_RLM_FALSE |
0.273 |
0.1311 |
0.1489 |
0.373 |
0.604 |
0.827 |
| T_Driven_fast_RLM_TRUE |
0.207 |
0.1933 |
0.0889 |
0.320 |
0.578 |
0.824 |
| T_Driven_pearson_LM_FALSE |
0.167 |
0.1778 |
0.1467 |
0.373 |
0.604 |
0.827 |
| T_Driven_pearson_LM_TRUE |
0.176 |
0.1578 |
0.0933 |
0.320 |
0.578 |
0.824 |
| T_Driven_pearson_RLM_FALSE |
0.320 |
0.3133 |
0.3044 |
0.409 |
0.613 |
0.827 |
| T_Driven_pearson_RLM_TRUE |
0.282 |
0.2689 |
0.2733 |
0.369 |
0.587 |
0.824 |
| T_Driven_spearman_LM_FALSE |
0.207 |
0.1889 |
0.1711 |
0.360 |
0.613 |
0.884 |
| T_Driven_spearman_LM_TRUE |
0.207 |
0.1800 |
0.0844 |
0.302 |
0.591 |
0.884 |
| T_Driven_spearman_RLM_FALSE |
0.269 |
0.2244 |
0.2267 |
0.369 |
0.620 |
0.884 |
| T_Driven_spearman_RLM_TRUE |
0.240 |
0.2489 |
0.1800 |
0.316 |
0.596 |
0.884 |
pander::pander(sparcityM)
| T_Blind_fast_LM_FALSE |
0.00526 |
0.00586 |
0.00790 |
0.00416 |
0.00330 |
0.00265 |
| T_Blind_fast_LM_TRUE |
0.00595 |
0.00987 |
0.01249 |
0.00457 |
0.00341 |
0.00266 |
| T_Blind_fast_RLM_FALSE |
0.00526 |
0.00586 |
0.00790 |
0.00416 |
0.00330 |
0.00265 |
| T_Blind_fast_RLM_TRUE |
0.00595 |
0.00987 |
0.01249 |
0.00457 |
0.00341 |
0.00266 |
| T_Blind_pearson_LM_FALSE |
0.00575 |
0.00769 |
0.00621 |
0.00413 |
0.00330 |
0.00265 |
| T_Blind_pearson_LM_TRUE |
0.00577 |
0.00914 |
0.01491 |
0.00462 |
0.00341 |
0.00266 |
| T_Blind_pearson_RLM_FALSE |
0.00448 |
0.00475 |
0.00481 |
0.00403 |
0.00331 |
0.00265 |
| T_Blind_pearson_RLM_TRUE |
0.00492 |
0.00487 |
0.00506 |
0.00454 |
0.00341 |
0.00266 |
| T_Blind_spearman_LM_FALSE |
0.00577 |
0.00577 |
0.00765 |
0.00416 |
0.00324 |
0.00249 |
| T_Blind_spearman_LM_TRUE |
0.00530 |
0.00531 |
0.01131 |
0.00438 |
0.00333 |
0.00249 |
| T_Blind_spearman_RLM_FALSE |
0.00492 |
0.00505 |
0.00574 |
0.00420 |
0.00329 |
0.00248 |
| T_Blind_spearman_RLM_TRUE |
0.00493 |
0.00495 |
0.00807 |
0.00449 |
0.00337 |
0.00248 |
| T_Driven_fast_LM_FALSE |
0.00472 |
0.00739 |
0.00828 |
0.00426 |
0.00327 |
0.00265 |
| T_Driven_fast_LM_TRUE |
0.00547 |
0.00575 |
0.01221 |
0.00462 |
0.00336 |
0.00266 |
| T_Driven_fast_RLM_FALSE |
0.00472 |
0.00739 |
0.00828 |
0.00426 |
0.00327 |
0.00265 |
| T_Driven_fast_RLM_TRUE |
0.00547 |
0.00575 |
0.01221 |
0.00462 |
0.00336 |
0.00266 |
| T_Driven_pearson_LM_FALSE |
0.00556 |
0.00561 |
0.00826 |
0.00425 |
0.00327 |
0.00265 |
| T_Driven_pearson_LM_TRUE |
0.00551 |
0.00587 |
0.01137 |
0.00467 |
0.00335 |
0.00266 |
| T_Driven_pearson_RLM_FALSE |
0.00444 |
0.00451 |
0.00460 |
0.00407 |
0.00327 |
0.00264 |
| T_Driven_pearson_RLM_TRUE |
0.00480 |
0.00481 |
0.00480 |
0.00442 |
0.00336 |
0.00265 |
| T_Driven_spearman_LM_FALSE |
0.00538 |
0.00539 |
0.00683 |
0.00417 |
0.00321 |
0.00248 |
| T_Driven_spearman_LM_TRUE |
0.00514 |
0.00539 |
0.01046 |
0.00446 |
0.00328 |
0.00248 |
| T_Driven_spearman_RLM_FALSE |
0.00490 |
0.00518 |
0.00550 |
0.00434 |
0.00322 |
0.00248 |
| T_Driven_spearman_RLM_TRUE |
0.00509 |
0.00509 |
0.00650 |
0.00438 |
0.00329 |
0.00248 |
pander::pander(Average_Latent_SizeM)
| T_Blind_fast_LM_FALSE |
3.12 |
2.63 |
5.57 |
2.83 |
2.25 |
2.33 |
| T_Blind_fast_LM_TRUE |
3.15 |
5.00 |
5.20 |
2.89 |
2.07 |
2.33 |
| T_Blind_fast_RLM_FALSE |
3.12 |
2.63 |
5.57 |
2.83 |
2.25 |
2.33 |
| T_Blind_fast_RLM_TRUE |
3.15 |
5.00 |
5.20 |
2.89 |
2.07 |
2.33 |
| T_Blind_pearson_LM_FALSE |
2.47 |
3.09 |
3.00 |
2.78 |
2.29 |
2.40 |
| T_Blind_pearson_LM_TRUE |
2.60 |
5.83 |
6.44 |
3.50 |
2.26 |
2.40 |
| T_Blind_pearson_RLM_FALSE |
2.35 |
2.52 |
2.91 |
2.32 |
2.35 |
2.20 |
| T_Blind_pearson_RLM_TRUE |
2.71 |
2.66 |
2.95 |
2.35 |
2.28 |
2.20 |
| T_Blind_spearman_LM_FALSE |
3.40 |
3.40 |
4.00 |
2.63 |
2.31 |
2.12 |
| T_Blind_spearman_LM_TRUE |
3.00 |
3.00 |
5.87 |
2.57 |
2.26 |
2.10 |
| T_Blind_spearman_RLM_FALSE |
2.71 |
2.90 |
3.12 |
2.35 |
2.44 |
2.00 |
| T_Blind_spearman_RLM_TRUE |
2.79 |
2.58 |
5.19 |
2.42 |
2.33 |
2.00 |
| T_Driven_fast_LM_FALSE |
3.00 |
2.67 |
4.00 |
3.38 |
2.41 |
2.38 |
| T_Driven_fast_LM_TRUE |
3.08 |
2.78 |
8.29 |
2.80 |
2.33 |
2.38 |
| T_Driven_fast_RLM_FALSE |
3.00 |
2.67 |
4.00 |
3.38 |
2.41 |
2.38 |
| T_Driven_fast_RLM_TRUE |
3.08 |
2.78 |
8.29 |
2.80 |
2.33 |
2.38 |
| T_Driven_pearson_LM_FALSE |
2.65 |
2.88 |
4.00 |
3.33 |
2.38 |
2.44 |
| T_Driven_pearson_LM_TRUE |
3.00 |
2.67 |
8.11 |
3.00 |
2.39 |
2.44 |
| T_Driven_pearson_RLM_FALSE |
2.40 |
2.77 |
2.92 |
2.47 |
2.38 |
2.33 |
| T_Driven_pearson_RLM_TRUE |
2.62 |
2.52 |
3.00 |
2.60 |
2.21 |
2.33 |
| T_Driven_spearman_LM_FALSE |
3.05 |
3.00 |
5.53 |
2.83 |
2.28 |
2.00 |
| T_Driven_spearman_LM_TRUE |
2.92 |
2.89 |
5.29 |
2.44 |
2.26 |
2.00 |
| T_Driven_spearman_RLM_FALSE |
2.73 |
2.85 |
2.57 |
2.76 |
2.33 |
2.00 |
| T_Driven_spearman_RLM_TRUE |
2.87 |
3.04 |
3.29 |
2.65 |
2.38 |
2.00 |
pander::pander(SigDeM)
| T_Blind_fast_LM_FALSE |
17 |
19 |
7 |
6 |
16 |
9 |
| T_Blind_fast_LM_TRUE |
13 |
7 |
5 |
9 |
14 |
9 |
| T_Blind_fast_RLM_FALSE |
17 |
19 |
7 |
6 |
16 |
9 |
| T_Blind_fast_RLM_TRUE |
13 |
7 |
5 |
9 |
14 |
9 |
| T_Blind_pearson_LM_FALSE |
15 |
11 |
12 |
9 |
17 |
10 |
| T_Blind_pearson_LM_TRUE |
10 |
6 |
9 |
12 |
19 |
10 |
| T_Blind_pearson_RLM_FALSE |
31 |
27 |
22 |
19 |
17 |
5 |
| T_Blind_pearson_RLM_TRUE |
24 |
29 |
22 |
20 |
18 |
5 |
| T_Blind_spearman_LM_FALSE |
20 |
20 |
13 |
19 |
16 |
8 |
| T_Blind_spearman_LM_TRUE |
13 |
10 |
15 |
14 |
19 |
10 |
| T_Blind_spearman_RLM_FALSE |
21 |
20 |
16 |
26 |
18 |
7 |
| T_Blind_spearman_RLM_TRUE |
19 |
19 |
21 |
26 |
18 |
8 |
| T_Driven_fast_LM_FALSE |
11 |
6 |
11 |
8 |
17 |
8 |
| T_Driven_fast_LM_TRUE |
12 |
9 |
7 |
10 |
15 |
8 |
| T_Driven_fast_RLM_FALSE |
11 |
6 |
11 |
8 |
17 |
8 |
| T_Driven_fast_RLM_TRUE |
12 |
9 |
7 |
10 |
15 |
8 |
| T_Driven_pearson_LM_FALSE |
26 |
8 |
8 |
9 |
16 |
9 |
| T_Driven_pearson_LM_TRUE |
9 |
6 |
9 |
11 |
18 |
9 |
| T_Driven_pearson_RLM_FALSE |
25 |
22 |
12 |
19 |
13 |
6 |
| T_Driven_pearson_RLM_TRUE |
24 |
21 |
15 |
15 |
14 |
6 |
| T_Driven_spearman_LM_FALSE |
21 |
10 |
15 |
12 |
18 |
9 |
| T_Driven_spearman_LM_TRUE |
13 |
9 |
21 |
9 |
19 |
10 |
| T_Driven_spearman_RLM_FALSE |
22 |
20 |
23 |
25 |
12 |
6 |
| T_Driven_spearman_RLM_TRUE |
23 |
23 |
24 |
20 |
16 |
7 |
pander::pander(La_SignificantM)
| T_Blind_fast_LM_FALSE |
32 |
31 |
16 |
34 |
81 |
123 |
| T_Blind_fast_LM_TRUE |
26 |
7 |
8 |
33 |
71 |
119 |
| T_Blind_fast_RLM_FALSE |
32 |
31 |
16 |
34 |
81 |
123 |
| T_Blind_fast_RLM_TRUE |
26 |
7 |
8 |
33 |
71 |
119 |
| T_Blind_pearson_LM_FALSE |
27 |
17 |
22 |
38 |
83 |
124 |
| T_Blind_pearson_LM_TRUE |
19 |
7 |
13 |
41 |
77 |
123 |
| T_Blind_pearson_RLM_FALSE |
59 |
45 |
44 |
56 |
85 |
113 |
| T_Blind_pearson_RLM_TRUE |
42 |
49 |
42 |
50 |
81 |
113 |
| T_Blind_spearman_LM_FALSE |
29 |
29 |
24 |
47 |
85 |
133 |
| T_Blind_spearman_LM_TRUE |
26 |
21 |
18 |
32 |
87 |
137 |
| T_Blind_spearman_RLM_FALSE |
33 |
32 |
28 |
64 |
89 |
132 |
| T_Blind_spearman_RLM_TRUE |
39 |
39 |
27 |
48 |
87 |
133 |
| T_Driven_fast_LM_FALSE |
31 |
15 |
31 |
53 |
98 |
126 |
| T_Driven_fast_LM_TRUE |
30 |
25 |
24 |
51 |
89 |
125 |
| T_Driven_fast_RLM_FALSE |
31 |
15 |
31 |
53 |
98 |
126 |
| T_Driven_fast_RLM_TRUE |
30 |
25 |
24 |
51 |
89 |
125 |
| T_Driven_pearson_LM_FALSE |
33 |
15 |
27 |
54 |
96 |
127 |
| T_Driven_pearson_LM_TRUE |
18 |
12 |
26 |
54 |
92 |
126 |
| T_Driven_pearson_RLM_FALSE |
53 |
38 |
42 |
70 |
94 |
124 |
| T_Driven_pearson_RLM_TRUE |
44 |
36 |
42 |
63 |
91 |
123 |
| T_Driven_spearman_LM_FALSE |
28 |
12 |
30 |
45 |
99 |
138 |
| T_Driven_spearman_LM_TRUE |
26 |
12 |
28 |
36 |
98 |
140 |
| T_Driven_spearman_RLM_FALSE |
35 |
24 |
41 |
66 |
93 |
133 |
| T_Driven_spearman_RLM_TRUE |
42 |
36 |
39 |
53 |
96 |
134 |
pander::pander(pbKNNaucM)
| T_Blind_fast_LM_FALSE |
0.947 |
0.951 |
0.928 |
0.910 |
0.928 |
0.936 |
| T_Blind_fast_LM_TRUE |
0.921 |
0.884 |
0.860 |
0.885 |
0.926 |
0.934 |
| T_Blind_fast_RLM_FALSE |
0.947 |
0.951 |
0.928 |
0.910 |
0.928 |
0.936 |
| T_Blind_fast_RLM_TRUE |
0.921 |
0.884 |
0.860 |
0.885 |
0.926 |
0.934 |
| T_Blind_pearson_LM_FALSE |
0.876 |
0.872 |
0.906 |
0.910 |
0.943 |
0.932 |
| T_Blind_pearson_LM_TRUE |
0.840 |
0.907 |
0.828 |
0.905 |
0.935 |
0.934 |
| T_Blind_pearson_RLM_FALSE |
0.911 |
0.919 |
0.925 |
0.942 |
0.919 |
0.954 |
| T_Blind_pearson_RLM_TRUE |
0.947 |
0.954 |
0.917 |
0.906 |
0.884 |
0.949 |
| T_Blind_spearman_LM_FALSE |
0.960 |
0.960 |
0.899 |
0.928 |
0.946 |
0.934 |
| T_Blind_spearman_LM_TRUE |
0.960 |
0.939 |
0.930 |
0.931 |
0.943 |
0.931 |
| T_Blind_spearman_RLM_FALSE |
0.913 |
0.919 |
0.926 |
0.921 |
0.934 |
0.929 |
| T_Blind_spearman_RLM_TRUE |
0.937 |
0.951 |
0.837 |
0.912 |
0.933 |
0.921 |
| T_Driven_fast_LM_FALSE |
0.945 |
0.918 |
0.969 |
0.931 |
0.939 |
0.943 |
| T_Driven_fast_LM_TRUE |
0.931 |
0.909 |
0.914 |
0.931 |
0.945 |
0.944 |
| T_Driven_fast_RLM_FALSE |
0.945 |
0.918 |
0.969 |
0.931 |
0.939 |
0.943 |
| T_Driven_fast_RLM_TRUE |
0.931 |
0.909 |
0.914 |
0.931 |
0.945 |
0.944 |
| T_Driven_pearson_LM_FALSE |
0.934 |
0.900 |
0.949 |
0.939 |
0.939 |
0.942 |
| T_Driven_pearson_LM_TRUE |
0.840 |
0.889 |
0.933 |
0.934 |
0.932 |
0.944 |
| T_Driven_pearson_RLM_FALSE |
0.876 |
0.888 |
0.926 |
0.936 |
0.926 |
0.947 |
| T_Driven_pearson_RLM_TRUE |
0.950 |
0.924 |
0.947 |
0.930 |
0.932 |
0.947 |
| T_Driven_spearman_LM_FALSE |
0.934 |
0.932 |
0.910 |
0.957 |
0.941 |
0.939 |
| T_Driven_spearman_LM_TRUE |
0.967 |
0.910 |
0.938 |
0.929 |
0.941 |
0.939 |
| T_Driven_spearman_RLM_FALSE |
0.925 |
0.920 |
0.943 |
0.956 |
0.916 |
0.927 |
| T_Driven_spearman_RLM_TRUE |
0.933 |
0.948 |
0.919 |
0.955 |
0.931 |
0.941 |
pander::pander(pbKNNaccM)
| T_Blind_fast_LM_FALSE |
0.77 |
0.736 |
0.805 |
0.701 |
0.724 |
0.724 |
| T_Blind_fast_LM_TRUE |
0.759 |
0.736 |
0.77 |
0.678 |
0.701 |
0.736 |
| T_Blind_fast_RLM_FALSE |
0.77 |
0.736 |
0.805 |
0.701 |
0.724 |
0.724 |
| T_Blind_fast_RLM_TRUE |
0.759 |
0.736 |
0.77 |
0.678 |
0.701 |
0.736 |
| T_Blind_pearson_LM_FALSE |
0.759 |
0.713 |
0.701 |
0.736 |
0.747 |
0.724 |
| T_Blind_pearson_LM_TRUE |
0.713 |
0.839 |
0.724 |
0.747 |
0.713 |
0.724 |
| T_Blind_pearson_RLM_FALSE |
0.759 |
0.747 |
0.77 |
0.736 |
0.713 |
0.724 |
| T_Blind_pearson_RLM_TRUE |
0.759 |
0.793 |
0.736 |
0.736 |
0.678 |
0.736 |
| T_Blind_spearman_LM_FALSE |
0.77 |
0.77 |
0.724 |
0.793 |
0.759 |
0.713 |
| T_Blind_spearman_LM_TRUE |
0.782 |
0.747 |
0.77 |
0.759 |
0.736 |
0.713 |
| T_Blind_spearman_RLM_FALSE |
0.747 |
0.759 |
0.77 |
0.793 |
0.724 |
0.724 |
| T_Blind_spearman_RLM_TRUE |
0.701 |
0.678 |
0.724 |
0.69 |
0.713 |
0.724 |
| T_Driven_fast_LM_FALSE |
0.759 |
0.816 |
0.793 |
0.77 |
0.747 |
0.724 |
| T_Driven_fast_LM_TRUE |
0.793 |
0.782 |
0.77 |
0.713 |
0.747 |
0.724 |
| T_Driven_fast_RLM_FALSE |
0.759 |
0.816 |
0.793 |
0.77 |
0.747 |
0.724 |
| T_Driven_fast_RLM_TRUE |
0.793 |
0.782 |
0.77 |
0.713 |
0.747 |
0.724 |
| T_Driven_pearson_LM_FALSE |
0.793 |
0.759 |
0.793 |
0.759 |
0.724 |
0.713 |
| T_Driven_pearson_LM_TRUE |
0.69 |
0.759 |
0.805 |
0.759 |
0.747 |
0.736 |
| T_Driven_pearson_RLM_FALSE |
0.713 |
0.701 |
0.77 |
0.724 |
0.77 |
0.713 |
| T_Driven_pearson_RLM_TRUE |
0.747 |
0.77 |
0.747 |
0.77 |
0.759 |
0.713 |
| T_Driven_spearman_LM_FALSE |
0.713 |
0.828 |
0.759 |
0.793 |
0.747 |
0.724 |
| T_Driven_spearman_LM_TRUE |
0.747 |
0.793 |
0.713 |
0.759 |
0.747 |
0.69 |
| T_Driven_spearman_RLM_FALSE |
0.736 |
0.759 |
0.724 |
0.77 |
0.736 |
0.713 |
| T_Driven_spearman_RLM_TRUE |
0.77 |
0.736 |
0.69 |
0.782 |
0.736 |
0.713 |
miny = 0.15
maxy = max(pbKNNaucM)
plot(thr,pbKNNaucM[1,],ylim=c(miny,maxy),
main="KNN's ROCAUC",
xlab="Correlation-Matrix's Maximum Goal",
ylab="ROC AUC",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
lines(thr,pbKNNaucM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
fastRows <- str_detect(rownames(pbKNNaucM),"fast")
pearsonRows <- str_detect(rownames(pbKNNaucM),"pearson")
spearmanRows <- str_detect(rownames(pbKNNaucM),"spearman")
T_BlindRows <- str_detect(rownames(pbKNNaucM),"T_Blind")
corRankRows <- str_detect(rownames(pbKNNaucM),"TRUE")
maxCorRankRows <- str_detect(rownames(pbKNNaucM),"FALSE")
RLMfitMethod <- str_detect(rownames(pbKNNaucM),"RLM")
meanAuc <- colMeans(pbKNNaucM[fastRows,])
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[pearsonRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[spearmanRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[!T_BlindRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[T_BlindRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[corRankRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[maxCorRankRows,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[RLMfitMethod,]))
meanAuc <- rbind(meanAuc,colMeans(pbKNNaucM[!RLMfitMethod,]))
legnames <- c("fast","Pearson","Spearman","T_Driven","T_Blind","SumCor","MaxCor","RLM","LM")
pbKNNaccM <- as.data.frame(pbKNNaccM)
pbKNNaccM[,1:ncol(pbKNNaccM)] <- sapply(pbKNNaccM,as.numeric)
Average_Latent_SizeM <- as.data.frame(Average_Latent_SizeM)
Average_Latent_SizeM[,1:ncol(Average_Latent_SizeM)] <- sapply(Average_Latent_SizeM,as.numeric)
Average_Latent_SizeM[is.na(Average_Latent_SizeM)] <- 0
SigDeM <- as.data.frame(SigDeM)
SigDeM[,1:ncol(SigDeM)] <- sapply(SigDeM,as.numeric)
sparcityM <- as.data.frame(sparcityM)
sparcityM[,1:ncol(sparcityM)] <- sapply(sparcityM,as.numeric)
miny = 0.65
maxy = max(meanAuc)+0.025
plot(thr,meanAuc[1,],ylim=c(miny,maxy),
main="Mean KNN's ROCAUC",
xlab="Correlation-Matrix's Maximum Goal",
ylab="ROC AUC",
type="l",
col=1,
lwd=2,
lty=1)
for (ind in 2:nrow(meanAuc))
{
lines(thr,meanAuc[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 0.35
maxy = max(pbKNNaccM) + 0.1
plot(thr,pbKNNaccM[1,],ylim=c(miny,maxy),
main="KNN's Accuracy",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Accuracy",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
lines(thr,pbKNNaccM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanAcc <- colMeans(pbKNNaccM[fastRows,])
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[pearsonRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[spearmanRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[!T_BlindRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[T_BlindRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[corRankRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[maxCorRankRows,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[RLMfitMethod,]))
meanAcc <- rbind(meanAcc,colMeans(pbKNNaccM[!RLMfitMethod,]))
miny = min(meanAcc)-0.01
maxy = max(meanAcc)+0.025
plot(thr,meanAcc[1,],ylim=c(miny,maxy),
main="Mean KNN's Accuracy",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Accuracy",
type="l",
col=1,
lwd=2)
for (ind in 2:nrow(meanAcc))
{
lines(thr,meanAcc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 1
maxy = 20*max(Average_Latent_SizeM)
plot(thr,Average_Latent_SizeM[1,],ylim=c(miny,maxy),
main="Average Size of Latent-Variable",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Size",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(Average_Latent_SizeM))
{
lines(thr,Average_Latent_SizeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(Average_Latent_SizeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanAccAvgSize <- colMeans(Average_Latent_SizeM[fastRows,])
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[pearsonRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[spearmanRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!T_BlindRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[T_BlindRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[corRankRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[maxCorRankRows,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[RLMfitMethod,]))
meanAccAvgSize <- rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!RLMfitMethod,]))
miny =1
maxy = 5*max(meanAccAvgSize)
plot(thr,meanAccAvgSize[1,],ylim=c(miny,maxy),
main="Mean Size of Average-Latent-Variable",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Size",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanAccAvgSize))
{
lines(thr,meanAccAvgSize[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(La_SignificantM)
maxy = 20*max(La_SignificantM)
plot(thr,La_SignificantM[1,],ylim=c(miny,maxy),
main="Number of Discovered Features",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Number of Features",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(La_SignificantM))
{
lines(thr,La_SignificantM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(La_SignificantM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanDiscovered <- colMeans(La_SignificantM[fastRows,])
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[pearsonRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[spearmanRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[!T_BlindRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[T_BlindRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[corRankRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[maxCorRankRows,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[RLMfitMethod,]))
meanDiscovered <- rbind(meanDiscovered,colMeans(La_SignificantM[!RLMfitMethod,]))
miny = min(meanDiscovered)
maxy = max(meanDiscovered) + 10
plot(thr,meanDiscovered[1,],ylim=c(miny,maxy),
main="Average Number of Discovered Features",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Number of Features",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanDiscovered))
{
lines(thr,meanDiscovered[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

SigDeM[is.na(SigDeM)] <- 0
miny = 1
maxy = 20*max(SigDeM)
plot(thr,SigDeM[1,],ylim=c(miny,maxy),
main="Number of Significant Latent Variables",
xlab="Correlation-Matrix's Maximum Goal",
ylab="How Many",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(SigDeM))
{
lines(thr,SigDeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(SigDeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
SigLatent <- colMeans(SigDeM[fastRows,])
SigLatent <- rbind(SigLatent,colMeans(SigDeM[pearsonRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[spearmanRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[!T_BlindRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[T_BlindRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[corRankRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[maxCorRankRows,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[RLMfitMethod,]))
SigLatent <- rbind(SigLatent,colMeans(SigDeM[!RLMfitMethod,]))
miny = 1
maxy = max(SigLatent) + 10
plot(thr,SigLatent[1,],ylim=c(miny,maxy),
main="Average # of Significant Latent Variables",
xlab="Correlation-Matrix's Maximum Goal",
ylab="How Many",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(SigLatent))
{
lines(thr,SigLatent[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

sparcityM[is.na(sparcityM)] <- 0
miny = min(sparcityM)
maxy = max(sparcityM) + 0.75
plot(thr,sparcityM[1,],ylim=c(miny,maxy),
main="Matrix Sparcity",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Sparcity",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(sparcityM))
{
lines(thr,sparcityM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(sparcityM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)
meanSparcity <- colMeans(sparcityM[fastRows,])
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[pearsonRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[spearmanRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[!T_BlindRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[T_BlindRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[corRankRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[maxCorRankRows,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[RLMfitMethod,]))
meanSparcity <- rbind(meanSparcity,colMeans(sparcityM[!RLMfitMethod,]))
miny = min(meanSparcity)
maxy = max(meanSparcity)+0.25
plot(thr,meanSparcity[1,],ylim=c(miny,maxy),
main="Mean Matrix Sparcity",
xlab="Correlation-Matrix's Maximum Goal",
ylab="Sparcity",
type="l",
col=1,
lwd=2,
log = "y")
for (ind in 2:nrow(meanSparcity))
{
lines(thr,meanSparcity[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)
